Blockchain-enabled resource management and sharing for 6G communications

The sixth-generation (6G) network must provide performance superior to previous generations to meet the requirements of emerging services and applications, such as multi-gigabit transmission rate, even higher reliability, and sub 1 ms latency and ubiquitous connection for the Internet of Everything (IoE). However, with the scarcity of spectrum resources, efficient resource management and sharing are crucial to achieving all these ambitious requirements. One possible technology to achieve all this is the blockchain. Because of its inherent properties, the blockchain has recently gained an important position, which is of great significance to 6G network and other networks. In particular, the integration of the blockchain in 6G will enable the network to monitor and manage resource utilization and sharing efficiently. Hence, in this paper, we discuss the potentials of the blockchain for resource management and sharing in 6G using multiple application scenarios, namely, Internet of things, device-to-device communications, network slicing, and inter-domain blockchain ecosystems

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Machine Learning-Powered Management Architectures for Edge Services in 5G Networks

L'abstract è presente nell'allegato / the abstract is in the attachmen

Integration and characterisation of the performance of fifth-generation mobile technology (5g) connectivity over the University of Oulu 5g test network (5gtn) for cognitive edge node based on fractal edge platform

Abstract. In recent years, there has been a growing interest in cognitive edge nodes, which are intelligent devices that can collect and process data at the edge of the network. These nodes are becoming increasingly important for various applications such as smart cities, industrial automation, and healthcare. However, implementing cognitive edge nodes requires a reliable and efficient communication network. Therefore, this thesis assesses the performance of direct cellular (5G) and IEEE 802.11-based Wireless Local Area Network (WLAN) technology for three network architectures, which has the potential to offer low-latency, high-throughput and energy-efficient communication, for cognitive edge nodes. The study focused on evaluating the network performance metrics of throughput, latency, and power consumption for three different FRACTAL-based network architectures. These architectures include IEEE 802.11-based last mile, direct cellular (5G) backbone, and IEEE 802.11-based last mile over cellular (5G) backbone topologies. This research aims to provide insights into the performance of 5G technology for cognitive edge nodes. The findings suggest that the power consumption of IEEE 802.11-enabled nodes was only slightly higher than the reference case, indicating that it is more energy-efficient than 5G-enabled nodes. Additionally, in terms of latency, IEEE 802.11 technology may be more favourable. The throughput tests revealed that the cellular (5G) connection exhibited high throughput for communication between a test node and an upper-tier node situated either on the internet or at the network edge. In addition, it was found that the FRACTAL edge platform is flexible and scalable, and it supports different wireless technologies, making it a suitable platform for implementing cognitive edge nodes. Overall, this study provides insights into the potential of 5G technology and the FRACTAL edge platform for implementing cognitive edge nodes. The results of this research can be valuable for researchers and practitioners working in the field of wireless communication and edge computing, as it sheds light on the feasibility and performance of these technologies for implementing cognitive edge nodes in various applications

A Framework for Developing Real-Time OLAP algorithm using Multi-core processing and GPU: Heterogeneous Computing

The overwhelmingly increasing amount of stored data has spurred researchers seeking different methods in order to optimally take advantage of it which mostly have faced a response time problem as a result of this enormous size of data. Most of solutions have suggested materialization as a favourite solution. However, such a solution cannot attain Real- Time answers anyhow. In this paper we propose a framework illustrating the barriers and suggested solutions in the way of achieving Real-Time OLAP answers that are significantly used in decision support systems and data warehouses